MACHINE LEARNING

Data Science in Tamil > Basic > MACHINE LEARNING
sklearn
Why Machine Learning is Important
in Data Science and AI

Machine Learning (ML) automates decision-making, enabling systems to learn from data without explicit programming.

It is a core engine of AI, powering applications like recommendation systems, image recognition, and predictive analytics.

  • ML algorithms can uncover hidden patterns and correlations in massive datasets that are impossible for humans to detect manually.

  • It enables predictive modeling, which helps businesses forecast trends and customer behaviors.

  • ML is used extensively in healthcare, finance, marketing, robotics, and cybersecurity, making it essential for real-world applications.

  • It supports natural language processing (NLP), speech, and vision tasks, enabling intelligent assistants, chatbots, and self-driving cars.

  • In Data Science, ML is the logical next step after data preprocessing and visualization, turning insights into actionable intelligence.

  • Mastery of ML tools like Scikit-learn, TensorFlow, and PyTorch enhances a data scientist’s ability to solve diverse and complex problems.

  • ML is key to building adaptive systems that improve their performance over time.

  • The field promotes a mindset of data-driven experimentation and innovation, critical for modern AI development.

ml
01

Module 1: Introduction to Machine Learning

  1. What is ML?

  2. ML vs AI vs Deep Learning

  3. Applications of ML in the real world

  4. Types of ML:

    1. Supervised

    2. Unsupervised

    3. Semi-supervised

    4. Reinforcement Learning

  5. Overview of ML workflow (Data → Model → Evaluate → Deploy)

02

Module 2: Python Tools for ML

  1. NumPy and Pandas recap for ML

  2. Data visualization tools: Matplotlib, Seaborn

  3. Scikit-learn: installation and architecture

  4. Jupyter notebooks and reproducible research

03

Module 3: Data Preprocessing

  1. Handling missing data

  2. Feature scaling (Normalization, Standardization)

  3. Encoding categorical variables (Label, One-Hot)

  4. Outlier detection and treatment

  5. Feature engineering and transformation

  6. Train-test split and cross-validation

04

Module 4: Supervised Learning – Regression

  1. Linear Regression:

    1. Simple and multiple linear regression

    2. Cost function, gradient descent

    3. Assumptions and diagnostics

  2. Polynomial Regression

  3. Regularization:

    1. Ridge, Lasso, ElasticNet

  4. Model evaluation:

    1. MSE, RMSE, R² score

    2. Cross-validation

05

Module 5: Supervised Learning – Classification

  1. Logistic Regression

  2. k-Nearest Neighbors (k-NN)

  3. Decision Trees

  4. Random Forests

  5. Support Vector Machines (SVM)

  6. Naive Bayes

  7. Model evaluation:

    1. Confusion Matrix, Precision, Recall, F1 Score, ROC-AUC

06

Module 6: Unsupervised Learning

  1. Clustering:

    1. K-Means

    2. Hierarchical Clustering

    3. DBSCAN

  2. Dimensionality Reduction:

    1. Principal Component Analysis (PCA)

    2. t-SNE (intro only)

  3. Applications:

    1. Customer segmentation

    2. Anomaly detection

07

Module 7: Model Selection and Evaluation

  1. Bias-Variance Tradeoff

  2. Overfitting and underfitting

  3. Cross-validation strategies (K-Fold, Stratified)

  4. Grid Search and Random Search

  5. Hyperparameter tuning

  6. Feature selection techniques

08

Module 8: Ensemble Learning

  1. Bagging vs Boosting

  2. Random Forest recap

  3. AdaBoost

  4. Gradient Boosting Machines (GBM)

  5. XGBoost

  6. Voting classifiers and stacking

09

Module 9: Advanced Topics

  1. Time Series Forecasting:

    1. Stationarity, ARIMA, rolling windows

  2. Recommender Systems:

    1. Content-based and collaborative filtering

  3. Anomaly Detection with Isolation Forest

  4. Introduction to Deep Learning:

    1. Perceptron

    2. Activation functions

    3. Neural networks

10

Module 10: Real-world ML Projects

  1. EDA + ML on Titanic Dataset

  2. House Price Prediction

  3. Customer Churn Prediction

  4. Fraud Detection using Classification

  5. Image Classification (Basic)

11

Module 11: Deployment Basics

  1. Saving and loading models (joblib, pickle)

  2. Introduction to ML deployment

  3. Streamlit / Flask for simple ML apps

  4. ML model lifecycle: training → validation → deployment → monitoring

12

Module 12: Ethics, Interpretability & Responsible AI

  1. AI bias and fairness

  2. Model explainability tools:

    1. SHAP, LIME

  3. Ethical implications of ML in real-world scenarios

  4. Reproducibility and version control in ML workflows

Click here to watch the recorded Data Analysis & Preprocessing playlist.

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